网络上的级联动态的时间依赖影响度量
James P Gleeson1, Ailbhe Cassidy1, Daniel Giles2
1University of Limerick, MACSI, Department of Mathematics and Statistics, Ireland.
Physical review. E
|June 19, 2025
概括
一个新的算法有效地计算了网络的预期级联大小. 这种方法在扩散过程中早期或晚期识别有影响力的节点,将以前的网络动态中心性措施概括为网络动态.
科学领域:
- 网络科学 网络科学
- 计算社会科学 计算社会科学
- 流行病学建模 流行病学建模
背景情况:
- 了解网络上传播的信息或疾病至关重要.
- 识别有影响力的节点是针对性干预的关键.
- 现有的中心性指标可能无法捕捉时间依赖的影响.
研究的目的:
- 提出和测试一个有效的算法来计算预期的级联大小.
- 根据它们在传播过程中的时间确定有影响力的节点.
- 在网络动态中对影响性节点识别的现有方法进行概括.
主要方法:
- 开发一种用于预期级联大小计算的新型算法.
- 测试不同动态模式 (关键和次关键) 中算法的准确性.
- 与非追溯中心性的比较,用于识别有影响力的单一传播者.
主要成果:
- 拟议的算法有效地计算了依赖时间的预期级联大小.
- 该措施准确地识别了有影响力的节点,区分了早期和晚期传播者.
- 该方法概括了非回溯中心性,在关键和次关键动态中证明有效.
结论:
- 开发的算法为分析网络级联动态提供了准确和高效的工具.
- 它增强了识别随着时间推移传播过程中的关键影响者的能力.
- 这项工作提供了对网络影响的中心性措施的概括方法.
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